The invention relates generally to imaging and automatically analyzing densely packed cell populations of biological materials.
Model systems are routinely employed to mimic the actual living environment in which biochemical processes take place. For example, cell cultures provide a simple in vitro system for manipulating and regulating genes, altering biochemical pathways, and observing the resulting effects in isolation. Such cell cultures play an important role in basic research, drug discovery, and toxicology studies.
Dense cell populations, including, cancer cells, cell and tissue cultures and biological samples are analyzed to extract a wide variety of information from these biological materials, such as, testing pharmaceuticals, imaging agents and therapeutics prior to testing in larger animals and humans, and to examine the progression of cancer and other diseases. In the case of cell cultures, the cells are often grown in vitro in 3D assays that are commonly imaged using widefield or confocal microscopes. The research results have traditionally been analyzed by studying the resulting image stacks.
Although others have segmented cells in a 3D environment, these efforts typically use a set of standard steps for separating the cells from the background, breaking the groups of cells into individual cells, and measuring the attributes of the cells. These approaches work best for high-resolution data and require modifications to be scalable.
Such 3D analysis tools enable the quantitative measurement of cell features as well as the statistical distributions of cells, which can lead to new insights. They also enable fast and repeatable analysis. The more physiologically relevant a model system is, the greater is its predictive value. 3D cell models provide a physiologically relevant context that accounts for cell-to-cell and cell-to-matrix interactions. For studying tumor growth, 3D cell cluster assays model positional effects such as cellular gradients of nutrients and oxygen, effect of metabolic stress on tumor growth, and therapeutic responsiveness. In contrast, two-dimensional (2D) monolayer cell cultures are easier to analyze, but do not model certain effects such as the tumor micro milieu.
Three-dimensional cell clusters are commonly imaged using confocal microscopy. The resulting confocal image stacks, known as z-stacks, are then traditionally studied manually to measure and analyze the experimental outcomes. Automating the analysis of these image stacks will enable researchers to use such cultures in a high-throughput environment. To date, most studies are limited either to simple measurements such as the total volume of the cluster, or to 2D measurements that are based on a single confocal slice. Recent studies have shown, that while global statistics are important, there is a wealth of information in different spatial contexts within cell clusters.